objs.m <- MapObjects(objs.i[["Mouse"]], objs.i[["Opossum"]], c("subclass", "type"), assay = "integrated")
Warning: Both reference and query assays have been processed with SCTransform.Setting normalization.method = 'SCT' and continuing.Normalizing query using reference SCT model
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 224 anchors
Filtering anchors
Retained 48 anchors
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
Predicting cell labels
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
| | 0 % ~calculating
Integrating dataset 2 with reference dataset
Warning: Number of anchors is less than k.weight. Lowering k.weight for sample pair.Finding integration vectors
Integrating data
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Computing nearest neighbors
Running UMAP projection
10:14:19 Read 7123 rows
10:14:19 Processing block 1 of 1
10:14:19 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:14:20 Initializing by weighted average of neighbor coordinates using 1 thread
10:14:20 Commencing optimization for 167 epochs, with 213690 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:14:25 Finished
Warning: Both reference and query assays have been processed with SCTransform.Setting normalization.method = 'SCT' and continuing.Normalizing query using reference SCT model
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 298 anchors
Filtering anchors
Retained 69 anchors
Finding integration vectors
Finding integration vector weights
Error in idx[i, ] <- res[[i]][[1]] :
number of items to replace is not a multiple of replacement length
obj.mouse <- ClusterSCT(obj.mouse, c(1))
obj.m.mouse <- ClusterSCT(obj.m.mouse, c(1))
obj.opossum <- ClusterSCT(obj.opossum, c(1))
obj.m.opossum <- ClusterSCT(obj.m.opossum, c(1))
obj.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_mouse.rds")
Loading required package: SeuratObject
Loading required package: sp
obj.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_opossum.rds")
objs.i.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_mouse.rds")
objs.i.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_opossum.rds")
DimPlot(obj.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, repel = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
obj.mouse$subclass.IT <- obj.mouse$subclass
obj.mouse$subclass.IT[obj.mouse$subclass %in% c("L2/3", "L4", "L5IT", "L6IT")] <- "IT"
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image

DimPlot(obj.m.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.m.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image

DimPlot(obj.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
obj.opossum$subclass.IT <- obj.opossum$subclass
obj.opossum$subclass.IT[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E")] <- "IT"
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)
Saving 7.29 x 4.51 in image



DimPlot(obj.m.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

DimPlot(obj.m.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

PlotMapping(list(objs.m.mouse[[1]], objs.m.opossum[[1]]), ident.order = c("IT", "L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"))
































PlotMapping(list(objs.m.mouse[[2]], objs.m.opossum[[2]]), ident.order = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", "Meis2"))
































PlotMapping(list(objs.m.mouse[[3]], objs.m.opossum[[3]]), ident.order = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC"))
































Idents(objs.m.opossum[[1]]) <- "subclass"
levels(objs.m.opossum[[1]]) <- c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", "L5NP", "L5PT", "L6CT", "L6b")
VlnPlot(objs.m.opossum[[1]], "predicted.subclass.score") + NoLegend()

Idents(objs.m.opossum[[2]]) <- "subclass"
levels(objs.m.opossum[[2]]) <- c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Meis2")
VlnPlot(objs.m.opossum[[2]], "predicted.subclass.score") + theme(legend.spacing.x = unit(2.0, 'cm'))

Idents(objs.m.opossum[[3]]) <- "subclass"
levels(objs.m.opossum[[3]]) <- c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")
VlnPlot(objs.m.opossum[[3]], "predicted.subclass.score") + theme(legend.spacing.x = unit(2.0, 'cm'))

Idents(obj.m.opossum) <- "subclass"
levels(obj.m.opossum) <- c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", "L5NP", "L5PT", "L6CT", "L6b",
"Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Meis2",
"Astro", "Micro", "OD", "OPC", "Endo", "VLMC")
VlnPlot(obj.m.opossum, "predicted.subclass.score") + NoLegend()

saveRDS(obj.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_mouse.rds")
saveRDS(obj.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_opossum.rds")
saveRDS(objs.i.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_i_mouse.rds")
saveRDS(objs.i.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_i_opossum.rds")
saveRDS(objs.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/objs_m_mouse.rds")
saveRDS(objs.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/objs_m_opossum.rds")
saveRDS(obj.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_m_mouse.rds")
saveRDS(obj.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_m_opossum.rds")
---
title: "R Notebook"
output: html_notebook
---


```{r}

library(Seurat)
library(SeuratDisk)
library(reticulate)
library(scrubletR)
library(ggplot2)
library(patchwork)
library(dplyr)
library(data.table)
library(clustree)
library(reshape2)
library(tidyr)
library(gridExtra)
library(stringr)
library(plyr)
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_integration_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/xgboost_train.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/plottingFxns.R")

classes <- c("Glutamatergic", "GABAergic", "Nonneuronal")
objs.mouse <- c()
objs.opossum <- c()
objs.i.mouse <- c()
objs.i.opossum <- c()
objs.m.mouse <- c()
objs.m.opossum <- c()

for (cl in classes) {

  obj.opossum <- readRDS(paste0("E:/Transcriptomics_V1/Opossum/seurat/opossum_v1_", tolower(cl), "_processed.rds"))
  obj.opossum$species <- "Opossum"
  obj.mouse.P38 <- readRDS(paste0("E:/Transcriptomics_V1/Mouse/seurat/mouse_v1_tasic_", tolower(cl), "_processed.rds"))
  obj.mouse.P38$species <- "Mouse"

  objs <- list(obj.opossum, obj.mouse.P38)
  obj.integrated <- IntegrateObjects(objs[[1]], objs[[2]], resolutions = c(1, 2), nfeatures = 3000, subsample = TRUE)
  objs.i <- SplitObject(obj.integrated, split.by = "species")
  objs.m <- MapObjects(objs.i[["Mouse"]], objs.i[["Opossum"]], c("subclass", "type"), assay = "integrated")
  objs.mouse <- append(objs.mouse, objs[[2]])
  objs.opossum <- append(objs.opossum, objs[[1]])
  objs.i.mouse <- append(objs.i.mouse, objs.i[["Mouse"]])
  objs.i.opossum <- append(objs.i.opossum, objs.i[["Opossum"]])
  objs.m.mouse <- append(objs.m.mouse, objs.m[[2]])
  objs.m.opossum <- append(objs.m.opossum, objs.m[[1]])
  
}

obj.mouse <- merge(objs.mouse[[1]], y = c(objs.mouse[[2]], objs.mouse[[3]]))
obj.opossum <- merge(objs.opossum[[1]], y = c(objs.opossum[[2]], objs.opossum[[3]]))
obj.m.mouse <- merge(objs.m.mouse[[1]], y = c(objs.m.mouse[[2]], objs.m.mouse[[3]]))
obj.m.opossum <- merge(objs.m.opossum[[1]], y = c(objs.m.opossum[[2]], objs.m.opossum[[3]]))

```


```{r}

obj.mouse <- ClusterSCT(obj.mouse, c(1))
obj.m.mouse <- ClusterSCT(obj.m.mouse, c(1))
obj.opossum <- ClusterSCT(obj.opossum, c(1))
obj.m.opossum <- ClusterSCT(obj.m.opossum, c(1))

```


```{r}

obj.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_mouse.rds")
obj.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_opossum.rds")
objs.i.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_mouse.rds")
objs.i.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_opossum.rds")
objs.m.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_mouse.rds")
objs.m.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_opossum.rds")
obj.m.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_mouse.rds")
obj.m.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_opossum.rds")

```


```{r}

DimPlot(obj.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

obj.mouse$subclass.IT <- obj.mouse$subclass
obj.mouse$subclass.IT[obj.mouse$subclass %in% c("L2/3", "L4", "L5IT", "L6IT")] <-  "IT"
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)

```


```{r}

DimPlot(obj.m.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)

```


```{r}

DimPlot(obj.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

obj.opossum$subclass.IT <- obj.opossum$subclass
obj.opossum$subclass.IT[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E")] <-  "IT"
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)

```


```{r}

DimPlot(obj.m.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

p <- DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
p <- DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)

```


```{r}

PlotMapping(list(objs.m.mouse[[1]], objs.m.opossum[[1]]), ident.order = c("IT", "L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"))

```


```{r}

PlotMapping(list(objs.m.mouse[[2]], objs.m.opossum[[2]]), ident.order = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", "Meis2"))

```


```{r}

PlotMapping(list(objs.m.mouse[[3]], objs.m.opossum[[3]]), ident.order = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC"))

```


```{r}

Idents(objs.m.opossum[[1]]) <- "subclass"
levels(objs.m.opossum[[1]]) <- c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", "L5NP", "L5PT", "L6CT", "L6b")
VlnPlot(objs.m.opossum[[1]], "predicted.subclass.score") + NoLegend()

```


```{r}

Idents(objs.m.opossum[[2]]) <- "subclass"
levels(objs.m.opossum[[2]]) <- c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Meis2")
VlnPlot(objs.m.opossum[[2]], "predicted.subclass.score") + theme(legend.spacing.x = unit(2.0, 'cm'))

```


```{r}

Idents(objs.m.opossum[[3]]) <- "subclass"
levels(objs.m.opossum[[3]]) <- c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")
VlnPlot(objs.m.opossum[[3]], "predicted.subclass.score") + theme(legend.spacing.x = unit(2.0, 'cm'))

```


```{r}

Idents(obj.m.opossum) <- "subclass"
levels(obj.m.opossum) <- c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", "L5NP", "L5PT", "L6CT", "L6b", 
                               "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Meis2", 
                               "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")
VlnPlot(obj.m.opossum, "predicted.subclass.score") + NoLegend()

```


```{r}

saveRDS(obj.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_mouse.rds")
saveRDS(obj.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_opossum.rds")
saveRDS(objs.i.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_i_mouse.rds")
saveRDS(objs.i.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_i_opossum.rds")
saveRDS(objs.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/objs_m_mouse.rds")
saveRDS(objs.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/objs_m_opossum.rds")
saveRDS(obj.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_m_mouse.rds")
saveRDS(obj.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-Tasic/obj_m_opossum.rds")

```

